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strategy-generate

Generate, modify, and backtest quantitative trading strategies with config.json and signal_engine.py.

6.1k|1.2k|Updated Jun 9, 2022
Search Tags:#optimization#strategy#signals#generate#backtest#quant
One-click install
npx skills add https://github.com/charliedream1/ai_quant_trade --skill strategy-generate-charliedream1
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: strategy-generate
Source: https://github.com/charliedream1/ai_quant_trade/tree/main/a_%E5%85%A8%E7%BD%91%E4%BC%98%E7%A7%80%E8%B5%84%E6%BA%90/10_%E5%A4%A7%E6%A8%A1%E5%9E%8B/07_skill%E5%8C%85/vibe_trading_skills/strategy-generate
Command: npx skills add https://github.com/charliedream1/ai_quant_trade --skill strategy-generate-charliedream1

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Automatically generate, modify, and optimize quantitative trading strategies, then backtest and evaluate their performance.

Core Features & Use Cases

  • End-to-end strategy generation: transform user intent into concrete strategy logic and configuration.
  • Backtesting & evaluation: run backtests across single or multiple assets, compare metrics, and iterate.
  • Iterative optimization: refine signals and risk controls based on backtest results to improve robustness.
  • Use Case: a quant researcher wants to design a dual moving-average crossover strategy, backtest it over a date range, and compare performance against a baseline.

Quick Start

Define config.json and implement code/signal_engine.py following the workflow, then run the backtest to evaluate results.

Frequently Asked Questions about strategy-generate

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate and backtest a quantitative trading strategy?▼

To generate and backtest a quantitative trading strategy, define a config.json, implement a signal_engine.py following the SignalEngine contract, and run the workflow for end-to-end execution and performance assessment.

Can I backtest multiple assets and compare performance metrics?▼

Yes, you can backtest multiple assets simultaneously. The workflow evaluates performance metrics across single or multi-asset backtests, enabling you to compare results against a baseline and iterate.

How do I optimize trading signals and risk controls after a backtest?▼

You optimize trading signals and risk controls through iterative refinement. The workflow evaluates backtest results, allowing you to modify and adjust strategy logic to improve overall robustness.

What is the best way to structure a config.json for a crossover strategy?▼

The best way to structure a config.json for a crossover strategy is to define your data inputs, signal parameters, and risk controls. This configuration drives the signal_engine.py implementation for end-to-end backtesting.

Do I need to implement a specific contract for the signal engine?▼

Yes, you need to implement code/signal_engine.py following the SignalEngine contract. This ensures your generated quantitative trading strategy integrates correctly with the backtest workflow for execution.

Does this workflow support modifying strategies based on backtest results?▼

Yes, the workflow supports modifying strategies based on backtest results. It automatically generates, modifies, and evaluates quantitative trading strategies to help refine signals and improve performance.

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